1. Introduction to Machine Learning
30th October 2006
Dr Bogdan L. Vrusias

2. Contents Learning Artificial Neural Networks Supervised Learning
Unsupervised Learning
30th October 2006
Bogdan L. Vrusias © 2006

3. What is Learning?
‘The action of receiving instruction or acquiring knowledge’.
‘A process which leads to the modification of behaviour or the acquisition of new abilities or responses, and which is additional to natural development by growth or maturation’.
Source: Oxford English Dictionary Online: accessed October 2003.
30th October 2006
Bogdan L. Vrusias © 2006

4. Machine Learning Negnevitsky: Callan: Luger:
‘In general, machine learning involves adaptive mechanisms that enable computers to learn from experience, learn by example and learn by analogy’ (2005:165)
Callan:
‘A machine or software tool would not be viewed as intelligent if it could not adapt to changes in its environment’ (2003:225)
Luger:
‘Intelligent agents must be able to change through the course of their interactions with the world’ (2002:351)
Learning capabilities can improve the performance of an intelligent system over time.
The most popular approaches to machine learning are artificial neural networks and genetic algorithms.
30th October 2006
Bogdan L. Vrusias © 2006

5. Types of Learning Inductive learning Evolutionary/genetic learning
Learning from examples
Evolutionary/genetic learning
Shaping a population of individual solutions through survival of the fittest
Emergent learning
Learning through social interaction: game of life
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Bogdan L. Vrusias © 2006

6. Inductive Learning Supervised learning Unsupervised learning
Training examples with a known classification from a teacher
Unsupervised learning
No pre-classification of training examples
Competitive learning: learning through competition on training examples
30th October 2006
Bogdan L. Vrusias © 2006

7. Key Concepts Learn from experience To adapt Generalisation
Through examples, analogy or discovery
To adapt
Changes in response to interaction
Generalisation
To use experience to form a response to novel situations
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Bogdan L. Vrusias © 2006

8. Generalisation
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9. Uses of Machine Learning
Techniques and algorithms that adapt through experience.
Used for:
Interpretation / visualisation: summarise data
Prediction: time series / stock data
Classification: malignant or benign tumours
Regression: curve fitting
Discovery: data mining / pattern discovery
30th October 2006
Bogdan L. Vrusias © 2006

10. Why Machine Learning? Complexity of task / amount of data
Other techniques fail or are computationally expensive
Problems that cannot be defined
Discovery of patterns / data mining
Knowledge Engineering Bottleneck
‘Cost and difficulty of building expert systems using traditional […] techniques’ (Luger 2002:351)
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11. Common Techniques Least squares Decision trees Support vector machines
Boosting
Neural networks
K-means
Genetic algorithms
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12. Decision Trees
A map of the reasoning process, good at solving classification problems (Negnevitsky, 2005)
A decision tree represents a number of different attributes and values
Nodes represent attributes
Branches represent values of the attributes
Path through a tree represents a decision
Tree can be associated with rules
30th October 2006
Bogdan L. Vrusias © 2006

13. Example 1 Consider one rule for an ice-cream seller (Callan 2003:241)
IF Outlook = Sunny
AND Temperature = Hot
THEN Sell
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Bogdan L. Vrusias © 2006

14. Example 1 Branch Root node Node Leaf Outlook Temperature Sunny Hot
Sell
Root node
Holiday Season
Overcast No
Don’t Sell
Node
Leaf
Don’t Sell
Sell
Yes No
Mild
Holiday Season
Cold
Don’t Sell
Yes
Temperature
Hot
Cold
Mild
Don’t Sell
Sell
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Bogdan L. Vrusias © 2006

15. Construction Concept learning:
Inducing concepts from examples
We can intuitively construct a decision tree for a small set of examples
Different algorithms used to construct a tree based upon the examples
Most popular ID3 (Quinlan, 1986)
30th October 2006
Bogdan L. Vrusias © 2006

16. Which Tree?
Different trees can be constructed from the same set of examples
Which tree is the best?
Based upon choice of attributes at each node in the tree
A split in the tree (branches) should correspond to the predictor with the maximum separating power
Examples can be contradictory
Real-life is noisy
30th October 2006
Bogdan L. Vrusias © 2006

17. Extracting Rules We can extract rules from decision trees
Create one rule for each root-to-leaf path
Simplify by combining rules
Other techniques are not so transparent:
Neural networks are often described as ‘black boxes’ – it is difficult to understand what the network is doing
Extraction of rules from trees can help us to understand the decision process
30th October 2006
Bogdan L. Vrusias © 2006

18. Issues Use prior knowledge where available
Not all the examples may be needed to construct a tree
Test generalisation of tree during training and stop when desired performance is achieved
Prune the tree once constructed
Examples may be noisy
Examples may contain irrelevant attributes
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Bogdan L. Vrusias © 2006

19. Artificial Neural Networks
An artificial neural network (or simply a neural network) can be defined as a model of reasoning based on the human brain.
The brain consists of a densely interconnected set of nerve cells, or basic information-processing units, called neurons.
The human brain incorporates nearly 10 billion neurons and 60 trillion connections, synapses, between them.
By using multiple neurons simultaneously, the brain can perform its functions much faster than the fastest computers in existence today.
30th October 2006
Bogdan L. Vrusias © 2006

20. Artificial Neural Networks
Each neuron has a very simple structure, but an army of such elements constitutes a tremendous processing power.
A neuron consists of a cell body, soma, a number of fibers called dendrites, and a single long fiber called the axon.
30th October 2006
Bogdan L. Vrusias © 2006

21. Artificial Neural Networks
Our brain can be considered as a highly complex, non-linear and parallel information-processing system.
Learning is a fundamental and essential characteristic of biological neural networks. The ease with which they can learn led to attempts to emulate a biological neural network in a computer.
30th October 2006
Bogdan L. Vrusias © 2006

22. Artificial Neural Networks
An artificial neural network consists of a number of very simple processors, also called neurons, which are analogous to the biological neurons in the brain.
The neurons are connected by weighted links passing signals from one neuron to another.
The output signal is transmitted through the neuron’s outgoing connection. The outgoing connection splits into a number of branches that transmit the same signal. The outgoing branches terminate at the incoming connections of other neurons in the network.
30th October 2006
Bogdan L. Vrusias © 2006

23. The Perceptron
The operation of Rosenblatt’s perceptron is based on the McCulloch and Pitts neuron model. The model consists of a linear combiner followed by a hard limiter.
The weighted sum of the inputs is applied to the hard limiter, which produces an output equal to +1 if its input is positive and 1 if it is negative.
30th October 2006
Bogdan L. Vrusias © 2006

24. The Perceptron How does the perceptron learn its classification tasks?
This is done by making small adjustments in the weights to reduce the difference between the actual and desired outputs of the perceptron.
The initial weights are randomly assigned, usually in the range [-0.5, 0.5], and then updated to obtain the output consistent with the training examples.
30th October 2006
Bogdan L. Vrusias © 2006

25. Multilayer neural networks
A multilayer perceptron is a feedforward neural network with one or more hidden layers.
The network consists of an input layer of source neurons, at least one middle or hidden layer of computational neurons, and an output layer of computational neurons.
The input signals are propagated in a forward direction on a layer-by-layer basis.
30th October 2006
Bogdan L. Vrusias © 2006

26. What does the middle layer hide?
A hidden layer “hides” its desired output.
Neurons in the hidden layer cannot be observed through the input/output behaviour of the network.
There is no obvious way to know what the desired output of the hidden layer should be.
Commercial ANNs incorporate three and sometimes four layers, including one or two hidden layers. Each layer can contain from 10 to 1000 neurons. Experimental neural networks may have five or even six layers, including three or four hidden layers, and utilise millions of neurons.
30th October 2006
Bogdan L. Vrusias © 2006

27. Supervised Learning
Supervised or active learning is learning with an external “teacher” or a supervisor who presents a training set to the network.
The most populat supervised neural network is the back-propagation neural network.
30th October 2006
Bogdan L. Vrusias © 2006

28. Back-propagation neural network
Learning in a multilayer network proceeds the same way as for a perceptron.
A training set of input patterns is presented to the network.
The network computes its output pattern, and if there is an error – or in other words a difference between actual and desired output patterns – the weights are adjusted to reduce this error.
30th October 2006
Bogdan L. Vrusias © 2006

29. Back-propagation neural network
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30. Back-propagation neural network
Network represented by McCulloch-Pitts model for solving the Exclusive-OR operation.
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31. Back-propagation neural network
(a) Decision boundary constructed by hidden neuron 3;
(b) Decision boundary constructed by hidden neuron 4;
(c) Decision boundaries constructed by the complete three-layer network
30th October 2006
Bogdan L. Vrusias © 2006

32. Unsupervised Learning
Unsupervised or self-organised learning does not require an external teacher.
During the training session, the neural network receives a number of different input patterns, discovers significant features in these patterns and learns how to classify input data into appropriate categories.
Unsupervised learning tends to follow the neuro-biological organisation of the brain.
Most popular unsupervised neural networks are the Hebbian network and the Self-Organising Feature Map.
30th October 2006
Bogdan L. Vrusias © 2006

33. Hebbian Network
Hebb’s Law can be represented in the form of two rules:
If two neurons on either side of a connection are activated synchronously, then the weight of that connection is increased.
If two neurons on either side of a connection are activated asynchronously, then the weight of that connection is decreased.
30th October 2006
Bogdan L. Vrusias © 2006

34. Competitive Learning
In competitive learning, neurons compete among themselves to be activated.
While in Hebbian learning, several output neurons can be activated simultaneously, in competitive learning, only a single output neuron is active at any time.
The output neuron that wins the “competition” is called the winner-takes-all neuron.
Self-organising feature maps are based on competitive learning.
30th October 2006
Bogdan L. Vrusias © 2006

35. What is a self-organising feature map?
Our brain is dominated by the cerebral cortex, a very complex structure of billions of neurons and hundreds of billions of synapses.
The cortex includes areas that are responsible for different human activities (motor, visual, auditory, somatosensory, etc.), and associated with different sensory inputs. We can say that each sensory input is mapped into a corresponding area of the cerebral cortex. The cortex is a self-organising computational map in the human brain.
The self-organising feature map has been introduced by Teuvo Kohonen and therefore is also called Kohonen network.
30th October 2006
Bogdan L. Vrusias © 2006

36. What is a self-organising feature map?
30th October 2006
Bogdan L. Vrusias © 2006

37. The Kohonen network
The Kohonen model provides a topological mapping. It places a fixed number of input patterns from the input layer into a higher-dimensional output or Kohonen layer.
Training in the Kohonen network begins with the winner’s neighbourhood of a fairly large size. Then, as training proceeds, the neighbourhood size gradually decreases.
30th October 2006
Bogdan L. Vrusias © 2006

38. The Kohonen network
The lateral connections are used to create a competition between neurons.
The neuron with the largest activation level among all neurons in the output layer becomes the winner.
This neuron is the only neuron that produces an output signal. The activity of all other neurons is suppressed in the competition.
The lateral feedback connections produce excitatory or inhibitory effects, depending on the distance from the winning neuron.
30th October 2006
Bogdan L. Vrusias © 2006

39. Competitive learning in the Kohonen network
To illustrate competitive learning, consider the Kohonen network with 100 neurons arranged in the form of a two-dimensional lattice with 10 rows and 10 columns.
The network is required to classify two-dimensional input vectors each neuron in the network should respond only to the input vectors occurring in its region.
The network is trained with 1000 two-dimensional input vectors generated randomly in a square region in the interval between –1 and +1.
30th October 2006
Bogdan L. Vrusias © 2006

40. Competitive learning in the Kohonen network
30th October 2006
Bogdan L. Vrusias © 2006